Title: Automatic Identification of Plant Physiological Disorders in Plant Factory Using Convolutional Neural Networks

Year of Publication: Aug - 2019
Page Numbers: 7-11
Authors: Shigeharu Shimamura, Kenta Uehara, Seiichi Koakutsu
Conference Name: The Fifth International Conference on Electronics and Software Science (ICESS2019)
- Japan


Plant factories with artificial light (PFAL) are attracting worldwide attention as a technology for stably producing crops. One of the problems of PFAL is tipburn which is a physiological disorder of crops. Especially, lettuce cultivated in PFAL has a high frequency of tipburn. When tipburn occurs, its identification is done by human eye observation, and tipburn leaves are trimmed by hand or that lettuce is removed from products. These operations require much labor and cost. In this study, we aim to perform binary discrimination of tipburn occurrence and its non-occurrence about lettuce cultivated in PFAL using machine learning with convolutional neural networks. In particular, we aim to recognize the symptom of tipburn which means the early stages of tipburn immediately before leaf tips discolor blackly and the commercial value as the vegetables is dameged. As a result of experiments, it is confirmed that recognition of the symptom of tipburn can be performed with high accuracy.